{"id":"W1987302087","doi":"10.1007/s11222-006-9009-4","title":"Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling","year":2007,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Stratified sampling; Metropolis–Hastings algorithm; Rejection sampling; Acceleration; Algorithm; Sampling (signal processing); Monte Carlo method; Mathematics; Gaussian; Set (abstract data type); Computer science; Extension (predicate logic); Markov chain Monte Carlo; Mathematical optimization; Statistics; Hybrid Monte Carlo","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000484622,0.00009092969,0.000153885,0.00004093512,0.0001853998,0.00005766984,0.00004969709,0.00004067577,0.00001053107],"category_scores_gemma":[0.0004430893,0.00006422849,0.00001867897,0.000098073,0.00007941796,0.00004248353,0.00004864795,0.00009401132,1.922398e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001465836,"about_ca_system_score_gemma":0.00001507267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003176606,"about_ca_topic_score_gemma":0.00002155315,"domain_scores_codex":[0.9991954,0.00003269935,0.000374898,0.0001154181,0.0001595649,0.0001220307],"domain_scores_gemma":[0.9988821,0.0006720899,0.0002031411,0.00009986652,0.0001068469,0.00003597495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004764076,0.0000530305,0.0005548321,0.000249984,0.00002007663,4.819107e-7,0.001140155,0.00001318208,0.003802479,0.9139354,0.00002754607,0.08019804],"study_design_scores_gemma":[0.0002154256,0.00002345726,0.001756224,0.00009832107,0.00003124203,0.00001004702,0.0007687298,0.7709197,0.003870622,0.2222019,0.00001212277,0.00009215762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3191681,0.00001437669,0.6804137,0.00001141353,0.00004599562,0.000115003,0.00001607012,0.00001002604,0.0002053851],"genre_scores_gemma":[0.5051946,0.000001935159,0.4947469,0.0000139334,0.00002328212,2.219461e-7,0.000002330032,0.000005713116,0.0000110764],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7709066,"threshold_uncertainty_score":0.2619162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08714820365819681,"score_gpt":0.3653813848639381,"score_spread":0.2782331812057413,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}